act_my_task is an open-weight model from Hedgehog. It has 52M parameters. At 16-bit it needs about 0.1 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.
This repository provides a trained Action Chunking with Transformers (ACT) policy for the SO-101 robotic manipulation platform. The policy is trained entirely from human teleoperation demonstrations collected with the LeRobot framework.
Runs On
What it takes to serve act_my_task (52M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.
| Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits |
|---|---|---|---|---|---|
| 16-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 8-bit | 0.1 GB | 0.1 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
| 4-bit | 0.0 GB | 0.0 GB | 1x MI300X (192 GB) Vultr |
$1.85 | 1x H100 $1.99 · 1x MI325X $2.00 |
Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 18, 2026.
act_my_task on every accelerator the SAVRN Index prices, at every precision
Model Card
This repository provides a trained Action Chunking with Transformers (ACT) policy for the SO-101 robotic manipulation platform. The policy is trained entirely from human teleoperation demonstrations collected with the LeRobot framework. Given visual observations and robot proprioceptive states, the policy predicts a sequence of future robot actions and executes the learned manipulation behavior autonomously. The goal of this project is to learn a manipulation policy directly from human demonstrations, without explicitly programming the robot trajectory. The trained policy learns the following behavior: Detect → Approach → Grasp → Transport → Place Specifically, the robot is trained to: 1.…
Excerpt from the card by Hedgehog.
Identity and Version
- Repository
- hedgehog678/act_my_task
- Publisher
- Hedgehog
- Task
- Not stated by the source
- Modality
- Other
- Library
- Not stated by the source
- Parameters
- 52M parameters
- Languages
- Not stated by the source
- Revision
- c15ef38b1896723290a425c6444d63e65b3c473f
- First published
- 2026-10-06
- Last updated
- 2026-10-07
Files and Weights
10 files, 207.3 MB in total. The weights are 3 files totalling 206.7 MB in safetensors.
Every file
| File | Type | Size | SHA-256 |
|---|---|---|---|
| model.safetensors | Weights | 206.7 MB | 56bdd40c73e0 |
| policy_postprocessor_step_0_unnormalizer_processor.safetensors | Weights | 6.6 KB | a2f4f8146e09 |
| policy_preprocessor_step_3_normalizer_processor.safetensors | Weights | 6.6 KB | 9b2069f84d67 |
| config.json | Configuration | 1.5 KB | — |
| policy_postprocessor.json | Configuration | 660 B | — |
| policy_preprocessor.json | Configuration | 1.2 KB | — |
| train_config.json | Configuration | 7.5 KB | — |
| README.md | Documentation | 6.6 KB | — |
| result.mp4 | Other | 579.5 KB | 861e36e47c8e |
| .gitattributes | Repository | 1.6 KB | — |
License and Download
- License
- Not stated by the source
- Access
- Open weights, no gate
- Download size
- 206.7 MB
Released by Hedgehog through its official repository on Hugging Face.
Memory Requirements
| Precision | Weights in memory |
|---|---|
| As published | 206.7 MB |
| 16-bit | 0.1 GB |
| 8-bit | 0.1 GB |
| 4-bit | 0.0 GB |
Weights only, from the published parameter count; the key-value cache and runtime add to this.
Questions About act_my_task
How much GPU memory does act_my_task need?
About 0.1 GB at 16-bit and 0 GB at 4-bit: the weights (52M parameters) plus a working margin. A long context needs more.
What is the cheapest GPU to run act_my_task on?
At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.